AI is now reading and scoring college applications. Admissions just crossed a line.
Students are told they can't use AI to write their essays. Meanwhile, several US colleges now use AI to read and score them. What started as small pilots is turning into a new layer in admissions review-fast, quiet, and easy to miss from the outside.
For education professionals, this isn't hype. It's workload, timelines, and policy colliding with new tools. The question isn't "if" AI will sit in your workflow-it already does. The work now is to make it accurate, fair, and explainable.
Where AI is already in play
Virginia Tech introduced an AI essay reader as a first pass on four short answers. AI provides one of two scores; a human steps in if there's a big gap between scores. With applications topping 57,000 for about 7,000 seats, the tool can scan around 250,000 essays in under an hour, helping the university release decisions roughly a month earlier.
Caltech is using AI to help verify submitted research. Applicants record AI-generated interviews about their work, which faculty later review. The goal: check whether a student can speak to the substance with clarity and interest.
The University of North Carolina at Chapel Hill faced pushback after reports that AI analyzed grammar and writing style. The institution clarified that AI creates data points, while humans make the final calls. Expect more of these public conversations as campuses refine their messaging.
Behind the scenes, Georgia Tech is deploying AI to read transfer transcripts and cut manual entry, with plans to extend to high school records. The team is also testing tools that flag likely Pell-eligible students who might have been missed. Stony Brook University uses AI to summarize essays and recommendations so counselors can spot context like caregiving or health issues faster.
Why colleges say they need it
Optional testing policies drove application surges. Even large reading teams can't keep pace without delays or inconsistencies. AI can standardize routine tasks and clear backlogs that used to eat weeks of staff time.
Leaders cite fewer administrative errors, faster turnarounds, and earlier decisions. The pitch is efficiency with guardrails. The hard part is maintaining trust while models do more of the first-pass interpretation.
The trust problem you must address
Professionals focused on admissions ethics are asking for clarity on use, limits, and oversight. Ruby Bhattacharya, who chairs the Admission Practices Committee at NACAC, has pressed for approaches grounded in transparency, fairness, and respect for student dignity. If you deploy AI, you need a policy the public can read and a process your team can defend.
Two truths can coexist: AI reduces noise and speeds decisions, and it can also introduce hidden bias or misread context. That means human oversight, clear rubrics, and routine audits aren't optional-they are the core of the system.
A practical playbook for admissions leaders
- Set principles first: publish what AI will and won't do, who reviews what, and how applicants can appeal.
- Keep humans in the loop: use AI as a first reader or summarizer; require human review on edge cases and split scores.
- Calibrate to your rubric: train reviewers with side-by-side reads; check inter-rater reliability between humans and AI.
- Audit for equity: test outputs by demographic group; watch error rates, false flags for integrity, and language/background effects.
- Log every decision input: prompts, model versions, scoring criteria, and overrides. You'll need this if challenged.
- Control your data: confirm FERPA compliance, retention limits, and whether vendors train on your data. Get it in writing.
- Start small: pilot on one component (short answers, transcript parsing), measure outcomes, and expand only if the metrics hold.
- Communicate early: tell students, counselors, and faculty how AI is used and how humans make final decisions.
- Create an appeal path: allow students to flag misreads, especially for context-heavy essays or unique experiences.
- Stress test: red-team for prompt injection, unusual writing styles, nonstandard transcripts, and disability-related accommodations.
Metrics to track every cycle
- Inter-rater reliability (human vs. AI and human vs. human)
- Time to first read and time to decision
- Exception rate (how often humans override AI)
- Demographic impact checks on scores and admit rates
- False positive rates on academic integrity flags
- Yield, melt, and scholarship alignment after changes
What to tell students and counselors
- What AI reviews (and what it doesn't), and where humans finalize decisions
- How context is considered, including summaries from essays and recommendations
- How to report an error or request a second look
- Whether using AI to write essays violates your policy and how you verify authenticity
What this means for your team
AI is already influencing the inputs that shape final decisions. Used well, it clears administrative friction and frees staff to focus on judgment and context. Used poorly, it erodes trust and creates new blind spots.
The sustainable path is a partnership: models handle volume and patterning; people handle nuance, context, and values. As Emily Pacheco noted, that mix works today-the bigger question is how far colleges will let models go next.
Resources
- US Department of Education: Artificial Intelligence and the Future of Teaching and Learning
- NACAC: Ethics in College Admission
Team upskilling
If your office is setting up AI literacy and workflow training, you can browse role-based options here: Courses by Job.
Bottom line: make AI visible, measurable, and accountable. If applicants can't understand the process, neither will your faculty, your president, or your accreditors.
Your membership also unlocks:
